Logistic Regression¶

In [1]:
import numpy as np
import pandas as pd
from pandas_profiling import ProfileReport

from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import Ridge, RidgeCV, Lasso, LassoCV, ElasticNet, ElasticNetCV, LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, confusion_matrix, roc_curve, roc_auc_score

import statsmodels.api as sm
from statsmodels.stats.outliers_influence import variance_inflation_factor

import matplotlib.pyplot as plt
import seaborn as sns
import pickle
In [6]:
df= pd.read_csv(r"D:/ML by Sudh/diabetes.csv")
df
Out[6]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome
0 6 148 72 35 0 33.6 0.627 50 1
1 1 85 66 29 0 26.6 0.351 31 0
2 8 183 64 0 0 23.3 0.672 32 1
3 1 89 66 23 94 28.1 0.167 21 0
4 0 137 40 35 168 43.1 2.288 33 1
... ... ... ... ... ... ... ... ... ...
763 10 101 76 48 180 32.9 0.171 63 0
764 2 122 70 27 0 36.8 0.340 27 0
765 5 121 72 23 112 26.2 0.245 30 0
766 1 126 60 0 0 30.1 0.349 47 1
767 1 93 70 31 0 30.4 0.315 23 0

768 rows × 9 columns

In [7]:
pf= ProfileReport(df)
pf.to_widgets()

Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]
Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]
Render widgets:   0%|          | 0/1 [00:00<?, ?it/s]
VBox(children=(Tab(children=(Tab(children=(GridBox(children=(VBox(children=(GridspecLayout(children=(HTML(valu…
In [8]:
df['BMI']= df['BMI'].replace(0, df['BMI'].mean())
In [9]:
df.columns
Out[9]:
Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',
       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],
      dtype='object')
In [10]:
df['BloodPressure']= df['BloodPressure'].replace(0, df['BloodPressure'].mean())
In [11]:
df['Insulin']= df['Insulin'].replace(0, df['Insulin'].mean())
In [12]:
df['Glucose']= df['Glucose'].replace(0, df['Glucose'].mean())
In [13]:
df['SkinThickness']= df['SkinThickness'].replace(0, df['SkinThickness'].mean())
In [16]:
fig, ax= plt.subplots(figsize= (10, 20))

sns.boxplot(data=df, ax=ax)
Out[16]:
<AxesSubplot:>
In [20]:
q= df['Insulin'].quantile(0.95)
df_new= df[df['Insulin']<q]

df_new
Out[20]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome
0 6 148.0 72.0 35.000000 79.799479 33.6 0.627 50 1
1 1 85.0 66.0 29.000000 79.799479 26.6 0.351 31 0
2 8 183.0 64.0 20.536458 79.799479 23.3 0.672 32 1
3 1 89.0 66.0 23.000000 94.000000 28.1 0.167 21 0
4 0 137.0 40.0 35.000000 168.000000 43.1 2.288 33 1
... ... ... ... ... ... ... ... ... ...
763 10 101.0 76.0 48.000000 180.000000 32.9 0.171 63 0
764 2 122.0 70.0 27.000000 79.799479 36.8 0.340 27 0
765 5 121.0 72.0 23.000000 112.000000 26.2 0.245 30 0
766 1 126.0 60.0 20.536458 79.799479 30.1 0.349 47 1
767 1 93.0 70.0 31.000000 79.799479 30.4 0.315 23 0

728 rows × 9 columns

In [21]:
fig, ax= plt.subplots(figsize= (10, 20))

sns.boxplot(data=df_new, ax=ax)
Out[21]:
<AxesSubplot:>
In [22]:
q= df['Insulin'].quantile(0.70)
df_new= df[df['Insulin']<q]

fig, ax= plt.subplots(figsize= (10, 20))

sns.boxplot(data=df_new, ax=ax)
Out[22]:
<AxesSubplot:>
In [23]:
q= df['Pregnancies'].quantile(0.98)
df_new= df[df['Pregnancies']<q]

fig, ax= plt.subplots(figsize= (10, 20))

sns.boxplot(data=df_new, ax=ax)
Out[23]:
<AxesSubplot:>
In [24]:
q= df['Insulin'].quantile(0.98)
df_new= df[df['Insulin']<q]

q= df['BMI'].quantile(0.99)
df_new= df_new[df_new['BMI']<q]

fig, ax= plt.subplots(figsize= (20, 20))

sns.boxplot(data=df_new, ax=ax)
Out[24]:
<AxesSubplot:>
In [25]:
q= df['Pregnancies'].quantile(0.98)
df_new= df[df['Pregnancies']<q]

q= df['BMI'].quantile(0.99)
df_new= df_new[df_new['BMI']<q]

q= df['SkinThickness'].quantile(0.99)
df_new= df_new[df_new['SkinThickness']<q]

fig, ax= plt.subplots(figsize= (20, 20))

sns.boxplot(data=df_new, ax=ax)
Out[25]:
<AxesSubplot:>
In [27]:
q= df['Pregnancies'].quantile(0.98)
df_new= df[df['Pregnancies']<q]

q= df['BMI'].quantile(0.99)
df_new= df_new[df_new['BMI']<q]

q= df['SkinThickness'].quantile(0.99)
df_new= df_new[df_new['SkinThickness']<q]

q= df['Insulin'].quantile(0.95)
df_new= df_new[df_new['Insulin']<q]

q= df['DiabetesPedigreeFunction'].quantile(0.99)
df_new= df_new[df_new['DiabetesPedigreeFunction']<q]

fig, ax= plt.subplots(figsize= (20, 20))

sns.boxplot(data=df_new, ax=ax)
Out[27]:
<AxesSubplot:>
In [28]:
ProfileReport(df_new)

Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]
Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]
Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]
Out[28]:
























In [29]:



     


q= df['Pregnancies'].quantile(0.98)
df_new= df[df['Pregnancies']<q]

q= df['BMI'].quantile(0.99)
df_new= df_new[df_new['BMI']<q]

q= df['SkinThickness'].quantile(0.99)
df_new= df_new[df_new['SkinThickness']<q]

q= df['Insulin'].quantile(0.95)
df_new= df_new[df_new['Insulin']<q]

q= df['DiabetesPedigreeFunction'].quantile(0.99)
df_new= df_new[df_new['DiabetesPedigreeFunction']<q]

q= df['Age'].quantile(0.99)
df_new= df_new[df_new['Age']<q]

df_new.head()



     






















    
    
Out[29]:











  
    

      
      Pregnancies
      Glucose
      BloodPressure
      SkinThickness
      Insulin
      BMI
      DiabetesPedigreeFunction
      Age
      Outcome
    

  
  
    

      0
      6
      148.0
      72.0
      35.000000
      79.799479
      33.6
      0.627
      50
      1
    

    

      1
      1
      85.0
      66.0
      29.000000
      79.799479
      26.6
      0.351
      31
      0
    

    

      2
      8
      183.0
      64.0
      20.536458
      79.799479
      23.3
      0.672
      32
      1
    

    

      3
      1
      89.0
      66.0
      23.000000
      94.000000
      28.1
      0.167
      21
      0
    

    

      5
      5
      116.0
      74.0
      20.536458
      79.799479
      25.6
      0.201
      30
      0
    

  



























In [ ]:



     


def outlier_removal(self, data):
    def outlier_limits(col):
        Q3, Q1= np.nanpercentile(col, [75, 25])
        IQR= Q3-Q1
        UL= Q3+1.5*IQR
        LL= Q1-1.5*IQR
        return UL, LL
    
    for columns in data.columns:
        if data[columns].dtype != 'int64':
            UL, LL= outlier_limits(data[columns])
            data[columns]= np.where((data[columns]>UL) | (data[columns]<LL),
                                   np.nan, data[columns])
    return data



     



















In [30]:



     


y= df_new['Outcome']
y



     






















    
    
Out[30]:








0      1
1      0
2      1
3      0
5      0
      ..
763    0
764    0
765    0
766    1
767    0
Name: Outcome, Length: 680, dtype: int64
























In [31]:



     


X= df_new.drop(columns=['Outcome'])
X



     






















    
    
Out[31]:











  
    

      
      Pregnancies
      Glucose
      BloodPressure
      SkinThickness
      Insulin
      BMI
      DiabetesPedigreeFunction
      Age
    

  
  
    

      0
      6
      148.0
      72.0
      35.000000
      79.799479
      33.6
      0.627
      50
    

    

      1
      1
      85.0
      66.0
      29.000000
      79.799479
      26.6
      0.351
      31
    

    

      2
      8
      183.0
      64.0
      20.536458
      79.799479
      23.3
      0.672
      32
    

    

      3
      1
      89.0
      66.0
      23.000000
      94.000000
      28.1
      0.167
      21
    

    

      5
      5
      116.0
      74.0
      20.536458
      79.799479
      25.6
      0.201
      30
    

    

      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    

    

      763
      10
      101.0
      76.0
      48.000000
      180.000000
      32.9
      0.171
      63
    

    

      764
      2
      122.0
      70.0
      27.000000
      79.799479
      36.8
      0.340
      27
    

    

      765
      5
      121.0
      72.0
      23.000000
      112.000000
      26.2
      0.245
      30
    

    

      766
      1
      126.0
      60.0
      20.536458
      79.799479
      30.1
      0.349
      47
    

    

      767
      1
      93.0
      70.0
      31.000000
      79.799479
      30.4
      0.315
      23
    

  



680 rows × 8 columns


























In [37]:



     


scalar = StandardScaler()
X_scaled = scalar.fit_transform(X)



     



















In [38]:



     


def vif_score(x):
    scaler = StandardScaler()
    arr = scaler.fit_transform(x)
    return pd.DataFrame([[x.columns[i], variance_inflation_factor(arr,i)] for i in range(arr.shape[1])], columns=["FEATURE", "VIF_SCORE"])



     



















In [39]:



     


vif_score(X)



     






















    
    
Out[39]:











  
    

      
      FEATURE
      VIF_SCORE
    

  
  
    

      0
      Pregnancies
      1.452236
    

    

      1
      Glucose
      1.306339
    

    

      2
      BloodPressure
      1.263154
    

    

      3
      SkinThickness
      1.484098
    

    

      4
      Insulin
      1.282659
    

    

      5
      BMI
      1.525553
    

    

      6
      DiabetesPedigreeFunction
      1.040328
    

    

      7
      Age
      1.666414
    

  



























In [40]:



     


x_train, x_test, y_train, y_test = train_test_split(X_scaled, y , test_size= 0.20 , random_state= 144)



     



















In [41]:



     


x_train



     






















    
    
Out[41]:








array([[-0.19442966, -0.27854254, -1.35040476, ..., -0.28624028,
         0.3666653 , -0.21887404],
       [ 0.13859848,  0.91971237,  1.09307712, ..., -0.47354249,
        -0.89655939, -0.49330738],
       [ 1.80373918, -1.61374088,  0.50327115, ..., -0.05211252,
        -0.58418589,  0.51294819],
       ...,
       [ 1.80373918,  2.22067485,  1.09307712, ..., -0.3018488 ,
         2.61850062,  1.51920376],
       [ 2.13676732, -1.51103331,  0.84030313, ...,  0.21323227,
        -0.64254138,  0.51294819],
       [ 0.80465476, -0.58666524,  1.5143671 , ...,  0.58783668,
         0.76828837, -0.4018296 ]])
























In [42]:



     


x_test



     






















    
    
Out[42]:








array([[ 0.47162662,  0.57735382,  1.00881912, ...,  0.47857706,
        -0.56358984,  0.23851486],
       [-0.86048595,  0.3719387 , -1.01337277, ..., -0.52036804,
         0.83007653, -1.04217405],
       [ 2.46979547,  0.81700481,  1.85139908, ...,  0.72831334,
        -0.67343546,  1.70215932],
       ...,
       [-0.52745781, -1.20291062, -0.24616423, ...,  0.0091631 ,
        -0.50180167, -1.04217405],
       [-0.86048595, -1.64797673, -2.02446872, ..., -1.80026645,
        -0.43658083, -0.95069627],
       [-0.86048595, -0.99749549, -0.84485678, ..., -0.73888728,
         0.44561685, -0.76774071]])
























In [43]:



     


x_test[0]



     






















    
    
Out[43]:








array([ 0.47162662,  0.57735382,  1.00881912,  1.74646698, -0.27366809,
        0.47857706, -0.56358984,  0.23851486])
























In [45]:



     


from sklearn.linear_model import LogisticRegression



     



















In [46]:



     


logr_liblinear = LogisticRegression(verbose=1, solver='liblinear')



     



















In [49]:



     


logr= logr_liblinear.fit(x_train, y_train )



     






















    
    






[LibLinear]























In [50]:



     


logr.predict_proba([x_test[1]])



     






















    
    
Out[50]:








array([[0.70315489, 0.29684511]])
























In [51]:



     


logr.predict([x_test[1]])



     






















    
    
Out[51]:








array([0], dtype=int64)
























In [52]:



     


logr.predict_log_proba([x_test[1]])



     






















    
    
Out[52]:








array([[-0.35217809, -1.21454478]])
























In [53]:



     


type(y_test)



     






















    
    
Out[53]:








pandas.core.series.Series
























In [54]:



     


y_test.iloc[1]



     






















    
    
Out[54]:








0
























In [55]:



     


y_test



     






















    
    
Out[55]:








402    1
507    0
24     1
751    0
444    1
      ..
448    1
564    0
60     0
97     0
353    0
Name: Outcome, Length: 136, dtype: int64
























In [56]:



     


logr = LogisticRegression(verbose=1)



     



















In [57]:



     


logr.fit(x_train,y_train)



     






















    
    






[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.9s finished










    
    
Out[57]:








LogisticRegression(verbose=1)
























In [58]:



     


y_pred_liblinear = logr_liblinear.predict(x_test)
y_pred_liblinear



     






















    
    
Out[58]:








array([0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1,
       1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,
       0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,
       0, 0, 0, 0], dtype=int64)
























In [59]:



     


y_pred_default = logr.predict(x_test)
y_pred_default



     






















    
    
Out[59]:








array([0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1,
       1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,
       0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,
       0, 0, 0, 0], dtype=int64)
























In [60]:



     


confusion_matrix(y_test, y_pred_liblinear)



     






















    
    
Out[60]:








array([[84, 10],
       [23, 19]], dtype=int64)
























In [61]:



     


def model_eval(y_true,y_pred):
    tn, fp, fn, tp = confusion_matrix(y_test,y_pred).ravel()
    accuracy= (tp+tn)/(tp+tn+fp+fn)
    precision= tp/(tp+fp)
    recall= tp/(tp+fn)
    specificity= tn/(fp+tn)
    F1_Score = 2*(recall * precision) / (recall + precision)
    result= {"Accuracy":accuracy,"Precision":precision,"Recall":recall,'Specficity':specificity,'F1':F1_Score}
    return result

model_eval(y_test, y_pred_liblinear)



     






















    
    
Out[61]:








{'Accuracy': 0.7573529411764706,
 'Precision': 0.6551724137931034,
 'Recall': 0.4523809523809524,
 'Specficity': 0.8936170212765957,
 'F1': 0.5352112676056338}
























In [62]:



     


model_eval(y_test, y_pred_default)



     






















    
    
Out[62]:








{'Accuracy': 0.7573529411764706,
 'Precision': 0.6551724137931034,
 'Recall': 0.4523809523809524,
 'Specficity': 0.8936170212765957,
 'F1': 0.5352112676056338}
























In [63]:



     


auc = roc_auc_score(y_test, y_pred_liblinear)



     



















In [64]:



     


roc_auc_score(y_test, y_pred_default)



     






















    
    
Out[64]:








0.672998986828774
























In [65]:



     


fpr, tpr, thresholds = roc_curve(y_test, y_pred_liblinear)



     



















In [66]:



     


plt.plot(fpr, tpr, color='orange', label='ROC')
plt.plot([0, 1], [0, 1], color='darkblue', linestyle='--',label='ROC curve (area = %0.2f)' % auc)
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic (ROC) Curve')
plt.legend()
plt.show()



     






















    
    































In [ ]: